Literature DB >> 25865932

Model-based computation of total stressed blood volume from a preload reduction manoeuvre.

Antoine Pironet1, Thomas Desaive2, J Geoffrey Chase3, Philippe Morimont4, Pierre C Dauby5.   

Abstract

Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid therapy, a primary treatment to manage acute circulatory failure. From an engineering point of view, it dictates the cardiovascular system's behavior in changing physiological situations. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a more straightforward method is developed using data from a preload reduction manoeuvre. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the five most sensitive parameters; and (3) adjust only these five parameters. Stressed blood volume was selected by the algorithm, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal root mean squared error of 29.2%. Computed stressed blood volume equals 486 ± 117 ml or 15.7 ± 3.6 ml/kg, which matches previous independent experiments on pigs, dogs and humans. The method proposed in this work thus provides a simple way to compute total stressed blood volume from usual hemodynamic data.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cardiovascular system; Fluid therapy; Mathematical model; Parameter identification; Stressed blood volume

Mesh:

Year:  2015        PMID: 25865932     DOI: 10.1016/j.mbs.2015.03.015

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  5 in total

1.  Assessment of Dynamic Changes in Stressed Volume and Venous Return during Hyperdynamic Septic Shock.

Authors:  Athanasios Chalkias; Eleni Laou; Nikolaos Papagiannakis; Vaios Spyropoulos; Evaggelia Kouskouni; Kassiani Theodoraki; Theodoros Xanthos
Journal:  J Pers Med       Date:  2022-04-29

2.  Minimally invasive estimation of ventricular dead space volume through use of Frank-Starling curves.

Authors:  Shaun Davidson; Chris Pretty; Antoine Pironet; Thomas Desaive; Nathalie Janssen; Bernard Lambermont; Philippe Morimont; J Geoffrey Chase
Journal:  PLoS One       Date:  2017-04-27       Impact factor: 3.240

Review 3.  Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.

Authors:  J Geoffrey Chase; Jean-Charles Preiser; Jennifer L Dickson; Antoine Pironet; Yeong Shiong Chiew; Christopher G Pretty; Geoffrey M Shaw; Balazs Benyo; Knut Moeller; Soroush Safaei; Merryn Tawhai; Peter Hunter; Thomas Desaive
Journal:  Biomed Eng Online       Date:  2018-02-20       Impact factor: 2.819

4.  Model-Based Weaning Tests for VA-ECLS Therapy.

Authors:  Simon Habran; Thomas Desaive; Philippe Morimont; Bernard Lambermont; Pierre C Dauby
Journal:  Comput Math Methods Med       Date:  2020-04-06       Impact factor: 2.238

5.  Creation and application of virtual patient cohorts of heart models.

Authors:  S A Niederer; Y Aboelkassem; C D Cantwell; C Corrado; S Coveney; E M Cherry; T Delhaas; F H Fenton; A V Panfilov; P Pathmanathan; G Plank; M Riabiz; C H Roney; R W Dos Santos; L Wang
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

  5 in total

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